Comprehensive survey of recent drug discovery using deep learning
Drug discovery based on artificial intelligence has been in the spotlight recently as it
significantly reduces the time and cost required for developing novel drugs. With the …
significantly reduces the time and cost required for developing novel drugs. With the …
Graph neural networks for automated de novo drug design
Highlights•GNN has attracted wide attention from the field of designing drug molecules.•The
applications of GNN in molecule scoring, molecule generation and optimization, and …
applications of GNN in molecule scoring, molecule generation and optimization, and …
Interactiongraphnet: A novel and efficient deep graph representation learning framework for accurate protein–ligand interaction predictions
Accurate quantification of protein–ligand interactions remains a key challenge to structure-
based drug design. However, traditional machine learning (ML)-based methods based on …
based drug design. However, traditional machine learning (ML)-based methods based on …
Protein–ligand docking in the machine-learning era
Molecular docking plays a significant role in early-stage drug discovery, from structure-
based virtual screening (VS) to hit-to-lead optimization, and its capability and predictive …
based virtual screening (VS) to hit-to-lead optimization, and its capability and predictive …
PubChem 2023 update
S Kim, J Chen, T Cheng, A Gindulyte, J He… - Nucleic acids …, 2023 - academic.oup.com
Abstract PubChem (https://pubchem. ncbi. nlm. nih. gov) is a popular chemical information
resource that serves a wide range of use cases. In the past two years, a number of changes …
resource that serves a wide range of use cases. In the past two years, a number of changes …
Three-dimensional convolutional neural networks and a cross-docked data set for structure-based drug design
PG Francoeur, T Masuda, J Sunseri, A Jia… - Journal of chemical …, 2020 - ACS Publications
One of the main challenges in drug discovery is predicting protein–ligand binding affinity.
Recently, machine learning approaches have made substantial progress on this task …
Recently, machine learning approaches have made substantial progress on this task …
FP-GNN: a versatile deep learning architecture for enhanced molecular property prediction
Accurate prediction of molecular properties, such as physicochemical and bioactive
properties, as well as ADME/T (absorption, distribution, metabolism, excretion and toxicity) …
properties, as well as ADME/T (absorption, distribution, metabolism, excretion and toxicity) …
Deep learning model for efficient protein–ligand docking with implicit side-chain flexibility
MR Masters, AH Mahmoud, Y Wei… - Journal of Chemical …, 2023 - ACS Publications
Protein–ligand docking is an essential tool in structure-based drug design with applications
ranging from virtual high-throughput screening to pose prediction for lead optimization. Most …
ranging from virtual high-throughput screening to pose prediction for lead optimization. Most …
The impact of supervised learning methods in ultralarge high-throughput docking
CN Cavasotto, JI Di Filippo - Journal of Chemical Information and …, 2023 - ACS Publications
Structure-based virtual screening methods are, nowadays, one of the key pillars of
computational drug discovery. In recent years, a series of studies have reported docking …
computational drug discovery. In recent years, a series of studies have reported docking …
Quantum machine learning framework for virtual screening in drug discovery: a prospective quantum advantage
Abstract Machine Learning for ligand based virtual screening (LB-VS) is an important in-
silico tool for discovering new drugs in a faster and cost-effective manner, especially for …
silico tool for discovering new drugs in a faster and cost-effective manner, especially for …